This invention relates generally to the field of magnetic resonance imaging (xe2x80x9cMRIxe2x80x9d) and, in particular, to the automation of the interpretation of information present in MRI images to detect brain lesions. More specifically, the present invention relates to a method and/or system of detecting hyperintense regions in MRI images that are suspected of being related to various brain pathologies, such as Alzheimer""s Disease, Multiple Sclerosis and like neurodegenerative conditions and/or determining volumetric measurements of cerebral anatomical regions.
Present day computerized methods of hyperintensity identification in brain magnetic resonance images either rely heavily on human intervention, or on simple thresholding techniques. Consequently, these methods lead to considerable variation in the quantification of brain hyperintensities depending on image parameters such as contrast.
A review of a number of the published computerized segmentation techniques reveals the following. The manual or semi-automatic techniques, requiring informed judgment on the part of an experienced diagnostician, will be discussed first followed by a discussion of techniques considered fully automatic that generally require little or no operator interaction.
Cline et al.6 present a technique to produce a 3D segmentation of the head employing T1 and T2 images. This method requires an initial visual classification of a relatively small sample of the tissue types in the T1 and T2 images by an experienced radiologist to begin. The accuracy of this initial input directly affects the accuracy of the succeeding classification algorithm, which classifies the remaining tissue based on a bivariate distribution model for each tissue type. The possible classification outputs are; background, brain, CSF, WM, lesion, tumor, arteries or veins. Once the initial classification is made, a feature map is constructed employing clustering based on the bivariate normal probability distribution. With this feature map, segmentation is performed by replacing the voxel with the above tissue label assigned by the probabilistic calculation. Upon the completion of the segmentation, the resulting surfaces are filtered twice using a 3D diffusion filter to smooth discontinuities created by misclassifications and to improve rendering. A 3D connectivity algorithm then extracts surfaces from this smoothed, segmented data set. Finally, a dividing cubes algorithm is used to process the voxels marked in the connectivity algorithm for display. This method is rated with only preliminary results for three selected patients; one normal, one MS case and one tumor. It is important to reiterate that success of the classification depends upon the operator""s level of expertise. This method may be most useful as a surgical planning tool, or perhaps as a visualization tool, which appears to be the original intent.
Hohol et al.7 have adopted a similar technique. This method begins by manually isolating the intracranial cavity using the PD and T2 images. Regions of interest (ROI) are then generated for brain parenchyma and CSF to aid the Expectation Maximization (EM) tissue classifier. Each image for a patient is registered to a time reference image, and the EM classifier is used to classify each pixel of each image as either WM, GM, CSF, or lesion tissue. This classification includes an unspecified correction for partial voluming artifacts. Individual lesions are fixed by the use of a 4 dimensional connectivity algorithm that is assumed to similar to Cline6 with the addition of a time dimension. With use of the time dimension element, lesion volumes are reported to change over time. No verification of the imaging technique is reported. However, the authors do report lesion burden correlation with the neuropsychological test scores of MS patients.
Wicks et al.8 employ two manually selected thresholds to perform white matter lesion (WML) detection. The first is set to separate brain tissue from the skull, while the second is set to identify all areas definable as lesions having intensities greater than the brain tissue. In order to preserve accurate boundaries, the threshold is set somewhat lower in value than that is required to identify the lesions. The outlines of the identified lesions are superimposed on the original T2 image to allow manual correction of misclassification. It is reported that this requirement poses no additional burden since it prevents the operator from having to outline each lesion manually. It is also stated that the established threshold can be used for any successive serial study of one patient provided that the intensity histograms from later studies are scaled to match that of the initial study. Overall, this semi-automated thresholding technique generates approximately half the inter-observer variation than purely manual outlining technique, but it is stated that visual assessment of the delineation of the lesions between the manual technique and this semi-automatic one are xe2x80x9cequally plausiblexe2x80x9d. The semi-automatic double threshold method is probably suitable for use with small patient sets in a non-critical research environment.
Wang et al.9 report a similar global thresholding technique that employs intensity correction of interslice scans. In practice a reference histogram is generated from some arbitrary scan slice. From the next scan slice, another histogram is generated. The (low) background intensities are cut from the histograms using al sharp cut-off windowing function with a Hanning slope. A manually chosen parameter defines the number of bins required for this cut-off line to reach its maximum value of 1.0. The second histogram is matched to the first by minimizing the squared differences between them. This process permits a more satisfactory selection of global thresholds for a patient, as all subsequent scan slices are matched in sequence. Although the technique undoubtedly subdues the intensity variations for WM lesions across scans, the accuracy of the resulting lesion volumes cannot be judged as no independent measures are reported.
Zijenbos et al.10 describe a semi-automatic method based on pattern recognition and built around a back-propagation artificial neural network (ANN). This is done to minimize the required input training points needed to achieve a successful tissue classification. The method begins with the use of an inter-cranial contour algorithm to remove the skull and incidental CSF, followed by an intensity correction algorithm to remove the shading, or intensity inhomogeneity artifact. This correction requires the operator to select 10 to 20 points in the WM that therefore strongly influences the detected lesion load. The intensity correction is followed by the same diffusion filter mentioned in Cline6 to enhance the signal/noise ratio. Finally, the tissue classification occurs using the ANN with 3 input nodes (T1,T2, PD) and five output nodes; background, WM, GM, CSF, and WML. One ""sample of each class input tissue class is presented in sequence to the ANN until convergence (segmentation) occurs. Upon the completion of classification by the ANN, postprocessing is required to remove the WML that occur in close proximity to GM along the sulci and correct the classification errors caused by misregistration of the T1 image. In addition, all WML smaller than 10 pixels are eliminated as they are assumed to be the result of noise or misregistration in the T1 image. This semi-automated technique is compared with a completely manual method using two different observers. Their published results relating inter-rater and intra-rater variation using the kappa statistic indicate that the two techniques are well correlated, neither showing an obvious advantage over the other with respect to the observers used in the study. Although the technique is technically sophisticated, it is limited by its requirement of expert knowledge on the part of the operator. An experienced diagnostician may find it useful as a visualization tool aiding for documenting disease progression.
Mitchell et al.11 have published a semi-automated technique that is similar in some respects to Zijbendos10. This method uses the k-nearest neighbor (kNN) classifier that is applied to operator selected image regions. The process begins with operator selection of pure CSF regions and known WM from multiple locations within the image to overcome intensity variations due to radio frequency (RF) inhomogeneity. Thus trained, the classifier is able to separate CSF and WM. In a similar manner, the kNN classifier is used to detect pixels representing lesion pathology. Consequently, the image is classified into WM, CSF, and WML without reference to GM. All pixels labeled as lesions are then highlighted on the MRI image for operator verification. This process is reported as requiring several iterations involving operator refinement of the classifications. It is reported that inter-rater reliability for this semi-automatic method is roughly half of that reported by the purely manual method, while showing a 25% improvement over the manual method with respect to intra-rater reliability. Therefore, very little diagnostic leverage is obtained since expert knowledge is required to pass judgment on lesion identification. It however does appear to be an excellent tool for documenting disease progression if used by an experienced neuroradiologist.
Mitchell et al.12 report another method which is a manual outlining technique that employs a novel use of the multispectral data to enhance clinical diagnosis. This method begins with routine outlining of the MS lesions by a radiologist. A tissue intensity analysis is subsequently performed with the assumption that the CSF characteristics will not change over time and thus can be used to calibrate, or normalize, the image intensities of the PD and T2 slices. The same technique is applied to xe2x80x9cnormal appearingxe2x80x9d WM regions to develop a cluster mean to better differentiate it from xe2x80x9cabnormalxe2x80x9d WM. An equivalent WM-GM (eW-G) spectrum is formed by projecting this 2D multispectral feature data onto a 1D axis using a principle component analysis. The unique feature of this technique is that the measured changes in the eW-G spectrum correlation well with disease progression especially when lesion size changes are not apparent. It is stated that the use of the eW-G spectra could relax the requirement for accurately defining lesion boundaries in the measure of disease progression. This technique would be applicable as a research or diagnostic tool to identify disease progression based on the xe2x80x9ceW-Gxe2x80x9d spectra, while specifically useful in diagnosis of MS with its relatively large diffuse lesions.
Samarasekera et al.13 report a semi-automatic method that utilizes the fuzzy connectedness principles to achieve lesion segmentation. This method begins by selecting 10 centrally located slices from within the imaged head and constructing an intensity histogram. From this, an empirical intensity threshold is set by finding the first pixel value which is greater in intensity than the second highest peak in the histogram, but has a bin count that is 93% less than that second highest peak. This threshold is used to create a binary volume containing only the brightest appearing pixels. The original 3D volume image is next thresholded at a low value to create another binary image corresponding to skull tissue. These two binary volume images are subsequently analyzed with a connected component algorithm and any components connected in both binary images are considered blood vessels and eliminated. Any remaining voxels in the first binary volume are thought to represent true and false positive lesions. A fuzzy connectedness algorithm is applied to the original volume image, using the previously derived binary volume image, as a template to determine membership grades based on neighborhood connectedness and intensity similarity. Fuzzy objects formed on the basis of the fuzzy threshold (40%) are tested for connection to the scalp binary volume image, and if connected are eliminated. Any fuzzy object with a membership of greater than 1500, or less than 7 voxels is also eliminated. Finally, any remaining objects are now superimposed on slice images for operator approval as lesions. Test results, using four neuroradiologists, show that this method has a detection sensitivity of 97% compared to the radiologists, and a false negative volume fraction of 1.3%, with false positives virtually non-existant. This technique, although requiring expert knowledge, demonstrates value as a diagnostic tool if the claimed false positive rate can be maintained.
Mitchell et al.14 describe a semi-automatic technique to segment MS lesions in 3D data sets using only the T2 and PD weighted images. The technique generates 2D histograms of operator defined ROI to allow feature space classification of these regions using either an interactive kNN classification algorithm or a maximum likelihood (ML) classifier. When used with the kNN classifier, two operator selected thresholds determine the classification parameters for each cluster based on proximity of the threshold to the data to be clustered. When used with the ML classifier, mean and covariance values from the tissue ROI are used to establish those same parameters for the data clusters. A principle component analysis is used to establish an elliptical region about the mean for classification of each cluster. Knowledge supplied by the operator is used to establish confidence intervals and vary the classification thresholds accordingly. With completion of classification using either method, the resulting tissue classes are assigned colors and applied to the 3D image. This method is not able to directly segment lesion without operator direction, but once manually detected, is able to quantify their volumes. The accuracy of the method""s ability to calculate volumes is measured against phantom studies with the assumption that larger measured volumes have less error. The technique requires an extensive amount of specialized knowledge from the operator, and is likely not to be useful except to test the concept of the classifying tools.
Vinitski et al.15 describe a similar method also based on a kNN classifier. The RF inhomogeneity is corrected in the T1,T2 and PD images by applying a correction matrix developed from imaging an oil filled cylinder. The 3D anisotropic diffusion filter is then applied to remove partial volume effects on image voxels, while leaving edges and small lesions relatively distinct. An expert observer is required to identify multiple samples of 8 different tissue types (as per Cline6) to initiate the kNN classifier, which employed a xe2x80x9ckxe2x80x9d of 20 for 2D classification, and a xe2x80x9ckxe2x80x9d of 40 for 3D classification. The resulting tissue classifications are then judged by 5 board certified neuroradiologists for accuracy. Statistical analysis of these rankings are used to assign a most probable tissue classification to each voxel. The resulting 2D or 3D images are then color coded by the expected tissue type. Finally, a connectivity algorithm is used to extract surfaces, and a dividing cubes algorithm is used to construct desired surfaces of interest. The accuracy of the technique is evaluated using volumetric phantoms with accuracies of 45 to 8% reported. It appears that the technique as described is able to differentiate MS pathology from gliosis and edema. In view of the extensive requirement of neurology expertise, this technique is of limited value in actual clinical applications.
Pannizzo et al.16 report a semi-automated method of MS lesion quantification that is histogram driven. The method using an edge following algorithm to remove skull tissue that is subsequently judged by an operator. And, if needed, selects a new threshold in case of rejection. Upon successful removal of the skull, a histogram of the remaining brain tissue is generated and presumed bimodal. The brain tissue is segmented into two distributions; a central distribution corresponding to WM/GM tissue, and higher intensity pixels that correspond to MS lesions and periventricular effusions (PVE). Thresholds are calculated from the histogram where fitted lines to the sides of the central distribution cross the horizontal axis. The operator is again asked to judge the performance of this step and make the appropriate corrections if needed. It is assumed that all of the higher intensity pixels, once segmented in the previous step, are identified as lesions, plaques, or edema. The relative accuracy of the WM/GM semi-automated segmentation is stated as being within 97% agreement for two different operators. No estimate of MS lesion accuracy is claimed. This technique, requiring a large amount of operator interaction, is unlikely to be judged useful in a clinical context.
Discases such as MS and AD pose a burden upon neuroradiologists since they are often required to establish a diagnostic judgment using rough estimates from many image slices. As a result, fully automatic techniques may reduce the workload of neuroradiologists and perhaps aid in treatment efficiency. To complete this review, a representative sample of fully automated lesion detection techniques is presented below.
Kapouleas17 describes an automated system that requires the use of a model or atlas to remove false positive lesions. The brain tissue is segmented from the skull using the PD images, while lesions are detected using a simple threshold in the T2 slices. An initial removal of false positives lesions is completed by measuring the average intensity of the each brain slice based on the outline derived during the PD segmentation. The threshold is adjusted such that all pixels not brighter than 30% of the average in both the PD and T2 slice are rejected. A 3D representation is constructed from the stacked PD segmentations and a xe2x80x9clocally deformablexe2x80x9d 3D geometric atlas is made to fit the imaged brain. Using the locations defined by this atlas, the remaining false positives are removed. This method is judged for validity by comparing lesion output counts/locations with that of radiologists. The technique achieves a reported 87% agreement with the radiologists for axial images while falling to 78% agreement for coronal images. The lack of detail in the description of the method and the relatively low reported detection sensitivities lead to the conclusion that this tool may not be as useful as the manual technique presented by Samaraskera13.
Li et al.18 report a knowledge-based method built around the fuzzy c-means algorithm (FCM). The algorithm relies exclusively on intensity class relationships derived from the T2 slice and is restricted to those 5 slices lying immediately above or below the central axial slice in a image set. The analysis begins with tissue classification based on the unsupervised fuzzy-c means (UFCM) algorithm that yields 10 possible output classifications. The authors report that the high number of output classes reduce the changes of misclassify tumor tissue from otherwise normal tissue (WM/GM). The encoded knowledge comprises the distributions derived found from case studies, relating class center intensities to the presence or absence of tissue type. The classification is dependent upon the proximity of tissues with respect to the lateral ventricles. The class centers define xe2x80x9cfocus of attentionxe2x80x9d areas in the slice being analyzed, which are used to remove skull tissue and locate WM. Once identified, WM is subjected to a shape analysis to test for abnormalities (tumor detection). With WM identified and presence of tumor tissue accepted or rejected, CSF is then identified followed by GM identification. There is no relative performance measurement reported for this technique since a limited number of slices/patients are analyzed as proof of concept. However, it appears most useful in detecting tumors of size large enough to cause some distortion in the expected shape of the classified WM.
Warfield et al.19 demonstrate a method that employs an anatomical atlas to segment cortical and sub-cortical structures and distinguish WM lesions. This method subjects the input slice to the 3D diffusion filter previously mentioned to correct for inhomogeneity. The skull is removed semi-automatically and the intracranial tissue is segmented by an EM algorithm. The anatomical atlas is co-registered with a classified 3D image and elastic matching is used to fit anatomical structures to the classified brain. The initial EM classifier and the elastically matched atlas can adequately identify all structures in the brain except for the cortex. The cortex is segmented by the use of a seed growing algorithm that is dependent upon a separate model. With all of the GM structures of the brain thus identified, the WM and any incident lesions are segmented by removing the GM and CSF tissues previously identified. In addition, a two-class minimum distance classifier is used on the identified WM. The performance of the cortex segmentation is compared against 5 different raters and is reported to achieve a 95% success rate against a best rater and 96% accuracy as measured with respect to a xe2x80x9cstandardxe2x80x9d cortex. The method is unique in that it segments all possible GM tissue before attempting to segment WM/WML thus avoiding the problem inherent in all methods that segment WM lesions based on pixel intensity criteria. There is no published information concerning its ability to distinguish WM lesions so the method cannot be judged.
Johnston et al.20 present an involved method of MS lesion detection that is limited to slices occurring adjacent to the central axial slice as in Li18 above. This method employs PD and T2 slices and performs 8 bit scaling on the data. The skull is removed and the image with the remaining intracranial tissue is filtered with a non-linear low-pass filter to correct for RF inhomogeneity. A variation of the iterated conditional modes (ICM) algorithm is applied to perform a preliminary 3D segmentation. This step yields a separate 3D image for each tissue type (4) and assigns to each voxel an 8 bit intensity value that represents the probability that the voxel belongs to that tissue class. The method requires initial operator selection of pure tissue samples to generate histograms for each tissue type. From this, neighborhood interaction parameters are found which indicate the strength of interaction between tissue types. The final segmentation is obtained with convergence of the ICM algorithm after 5 to 8 iterations. Misclassifications, due to partial volume effects, force the use of at least two stages of post processing to enhance lesion detection. The first step is the merging of the two (PD and T2) 3D WM probability maps generated by ICM. The second is the re-application of ICM to this merged data set to generate a WM/WM lesion mask allowing the segmentation of lesions from WM without interference from GM intensities. Using a similarity index as in Zijdenbos10 discussed above, it is reported that the best accuracy, with respect to a manual outlining by an experienced operator, occurs on the 3 most central slices of the data set while decreasing significantly for other slices. This inability to reliably detect across a significant span of slices reduces the usefulness of the technique.
Goldberg-Zimring et al.2 demonstrate an automated technique using an ANN classifier combined with an anatomical map. This method is built on the assumptions that MS lesions in T2 and PD weighted scans are much brighter than other brain tissue, that non MS regions of the brain will be very large or very small in size, that MS lesions are relatively circular, and that most of the MS lesions occur almost exclusively in the periventricular WM. In operation, the subject image is first normalized so that its maximum pixel intensity is equal to 1. A sliding window thresholder is then applied to the image and returns a xe2x80x9c1xe2x80x9d for the central pixel of the window if that image pixel has a value greater than 0.5, otherwise is xe2x80x9c0xe2x80x9d. These binary regions are contoured, and the area, perimeter, and shape factor for each is calculated. Contoured areas with shape factors less than 0.2, or that have areas of less than 240 pixels extent are rejected as artifacts. All contoured objects lying within the cortex, or along the hemispherical fissure are also eliminated. This preliminary artifact filtering is supplemented by an ANN with three input and two output channels. Using empirical evidence that most MS lesions have high average pixel intensities coupled with a high shape index, the ANN is trained to reject contoured objects that do not meet the requirements of: 1) high average pixel intensity, 2) high shape factor, 3) high product of shape factor times average intensity. The ANN is supplemented by an unspecified post-processing stage that removed artifacts caused by ANN. The reported sensitivity of 0.87 and specificity of 0.96 for this technique is based on a group of 45 images. The technique produces a vast number of false positive lesions before the application of the ANN and might be significantly improved by employing a more sophisticated thresholding technique. The lack of specification of the final post-processing stage that corrects the ANN output raises some questions about the veracity of the sensitivity and specificity reports.
DeCarli et al.21 describes a fully automatic global thresholding method for segmenting WM hyperintensities. After application of a diffusion filter to remove RF inhomogeneity, a segmentation22 of the PD image is performed to identify cerebral cortex, CSF, and brain volumes. PD image pixels representing brain tissue are added to T2 image pixels representing brain tissue and a histogram of the combined images is generated. The histogram is modeled as a Gaussian allowing application of standard statistical moments to the pixel distribution. All pixels having intensities greater than 3 standard deviations above the mean for this distribution are considered to to be WM hyperintensities. A claim is made for significant correlation between lesion load detected in this manner and as outlined by an operator (r=0.83, p less than 0.001). This is somewhat misleading because this reliability measure is based on the measurement of CSF produced in a previous publication22 and does not directly relate to the specification of WM lesions reported here.
Brunetti et al.23-25 present a simple technique (xe2x80x9cQuantitative Magnetic Color Imagingxe2x80x9d) that can be considered fully automated as the operator is only asked to view the final output image. This method employs T1, T2 and PD images and calculates relaxation rate maps for each, based on the selection of different repetition times (TR) parameters during initial image acquisition. These parametric maps are then color coded (red for 1/T1, green for 1/T2, blue for PD) and combined into one multispectral color image. The colored image is displayed using a predefined color map in which the colors directly reflect the calculated parameter values. A violet-blue color on the map indicates suspected lesions. Inter-rater tests, using three raters of different levels of experience, indicate that this technique enhances visual detectability for all observers (k=0.66 with this technique vs k=0.56 for same observer without it). The accuracy of detection, using the kappa statistic, indicate good to substantial agreement for the two less experienced observers as compared to the expert neuroradiologist. This is a unique method, but the required manipulation of MR parameters during image acquisition limits its applicability.
It would be desirable to have a system and/or method for determining volumetric measurements of cerebral regions, automatically or free of subjective intervention by a user. By way of illustration, the instant invention calculates volumetric measurements of subcortical regions of a brain. Advantageously, such calculated volumetric measurements are used, for example, to indicate atrophy during a pathological process. Such a pathological process optionally includes a dementia evidencing a physical change in the brain. For instance, using the instant invention, one can recognized atrophy or volume shrinkage in a hippocampus coincident with an Alzheimer""s disease process.
It would be desirable to have a system and/or method for detecting lesion tissue in cerebral regions, automatically or free of subjective intervention by as user. By way of illustration, the instant invention detects lesion tissue in subcortical regions of a brain, which may indicate a dementia, such as, Alzheimer""s disease, Parkinson""s disease, Huntington""s disease, and a purely aging-associated dementia.
It would also be desirable to have a system and/or method of providing a cerebral region template generated from a patient""s own morphology. For example, such a template optionally enhances accurate location of regions of interest in the brain and accurate analysis thereof.
It is, therefore, a feature and advantage of the present invention to provide a method and/or system for detecting hyperintense regions in MRI images which addresses the problems associated with human subjectivity and existing thresholding techniques. In particular, the present invention introduces the concept of the use of knowledge guided rules and methods for automatically locating certain anatomical regions and detecting hyperintensities associated with same, which in turn are candidates for possible lesions.
It is another feature and advantage of the present invention to provide a method and/or system for detecting white matter hyperintensities and subcortical hyperintensity regions in MRI images which is designed for high sensitivity, detection and monitoring of subtle (small) brain lesions in patients with neurodegenerative diseases.
It is also a feature and advantage of the present invention to provide an automated method and/or automated system for detecting hyperintensities in MRI images which requires no a priori knowledge of the intensity distributions of image pixels and which does not require any additional direction from an expert operator to perform its task.
Yet another feature and advantage of the present invention is to provide a method and/or system for processing the information and/or data provided by MRI images to detect and identify hyperintensities for disease screening, and thereby serve as a diagnostic aid in helping neuroradiologists verify disease diagnosis and severity, to arrive at a prognosis, or to follow the possible effects of therapy (e.g., drug therapy).
The above features and advantages are accomplished, for example, by an automated, knowledge-guided hyperintensity detection (KGHID) method or system that uses encoded knowledge of brain anatomy and MRI characteristics of individual tissues to reclassify pixels from an initial unsupervised tissue classification.
The method/system herein described optionally requires no more than a reliable initial segmentation of brain tissues into classes of, for example, cerebral spinal fluid, white matter, gray matter and mixed boundary tissue. KGHID is then able to identify subcortical structures and hyperintense lesions using these tissue classes and encoded anatomical knowledge. This knowledge consists of pixel intensity relationships as found in the classified tissues.
Another feature and advantage of this method or system is its ability to detect automatically lesions confined within white matter tissue around the lateral ventricles or within desired subcortical structures. Lesions in these areas are thought to be attributable to various neurodegenerative diseases. The method detects hyperintensities within various tissues in the brain, including but not limited to potential lesions in white matter, a periventricular ring, and possible lesions in lenticular nuclei.
The structures already delineated as part of the analysis form the basis for further implementing lesion detection within the caudate nuclei and the thalamus. In addition, the method/system requires no initial assumptions about the relative health of the brain being analyzed. The present technique has been proven to work with in vivo and in vitro brain images. The method/system of the present invention is preferable applied to axial images. It could also be adapted for use with coronal, sagittal, or like images.
Moreover, since the method/system of the present invention requires no operator intervention nor reference to an anatomical atlas, it is ideally suited to real-time imaging. While the algorithm described herein is written in IDL, a computer language suited to prototyping, it could be easily ported into C or other software languages.
It is a feature and advantage of the instant invention to provide a system and/or method for determining volumetric measurements of cerebral regions, automatically or free of subjective intervention by a user. By way of illustration, the instant invention calculates volumetric measurements of subcortical regions of a brain. Advantageously, such calculated volumetric measurements are used, for example, to indicate atrophy during a pathological process. Such a pathological process optionally includes a dementia evidencing a physical change in the brain. For instance, using the instant invention, I have recognized atrophy or volume shrinkage in a hippocampus coincident with an Alzheimer""s disease process.
It is another feature and advantage of the instant invention to provide a system and/or method for detecting lesion tissue in cerebral regions, automatically or free of subjective intervention by as user. By way of illustration, the instant invention detects lesion tissue in subcortical regions of a brain, which may indicate a dementia, such as, Alzheimer""s disease, Parkinson""s disease, Huntington""s disease, and: a purely aging-associated dementia.
It is yet another feature and advantage of the instant invention to provide a system and/or method of providing a cerebral region template generated from a patient""s own morphology. For example, such a template optionally enhances accurate location of regions of interest in the brain and accurate analysis thereof.
It is another feature and advantage of the instant invention to provide a system and/or method for performing serial images separated in time to monitor disease progression and/or success of therapy.
More specifically, the instant invention provides a method of interpreting at least one imaging scan of a patient. The method includes the following sequential, non-sequential, or sequence-independent steps. A processor, for example, (a) identifies a location of at least one cerebral region in the imaging scan based, at least in part, on a relative location of a lateral ventricle. The identifying step (a) is free of human intervention and/or is automatic. Advantageously, such a step at least substantially eliminates human subjectivity, which may vary from user to user.
Optionally, the at least one imaging scan includes a plurality of consecutive imaging scans. Optionally, the processor (b) identifies a location of each cerebral region of the at least one cerebral region in a successive imaging scan of the plurality of imaging scans based, at least in part, on a location of a corresponding cerebral region in a preceding imaging scan of the plurality of imaging scans, the identifying step (b) is free of human intervention and/or is automatic. Optionally, the processor (c) determines a volumetric measurement for at least one of the cerebral regions.
Optionally, the processor (d) identifies a suspected presence of white matter lesion tissue in the imaging scan based on a knowledge base relating voxel intensity distributions and a spatial relationship of cerebral anatomical structures.
Optionally, at least one cerebral region includes at least one of a caudate nucleus, a lenticular nucleus, a thalamus, a hippocampus, a periventricular ring, white matter, a brain stem, and a cerebellum.
Optionally, the identifying step (a) includes determining a location of the caudate nucleus, at least in part, from the relative location of the lateral ventricle. Optionally, the identifying step (a) includes determining a location of the thalamus, at least in part, from at least one of a relative location of the caudate nucleus and the relative location of the lateral ventricle. Optionally, the identifying step (a) includes determining a location of the lenticular nucleus, at least in part, from at least one of the relative location of the caudate nucleus and a relative location of the thalamus.
Optionally, the identifying step (a) includes determining a location of the hippocampus, at least in part, from the relative location of the lateral ventricle.
Optionally, the identifying step (a) includes determining a location of the periventricular ring, at least in part from at least one of the location of the lateral ventricle, the location of the caudate nucleus, and the location of the thalamus.
Optionally, the identifying step (d) includes identifying white matter regions in the at least one imaging scan, eliminating therefrom voxels identifiable as at least one of white matter, a perivascular space, and cerebrospinal fluid.
Optionally, the white matter regions include at least one of an internal capsule, a frontal lobe, an anterior temporal lobe, an anterior parietal lobe, a posterior parietal lobe, an occipital lobe, and a posterior temporal lobe.
Optionally, the processor generates a template unique to the patient, the template including each identified at least one cerebral region. Optionally, the at least one imaging scan includes a plurality of consecutive imaging scans. Optionally, the processor (b) identifies a location of each cerebral region of the at least one cerebral region in a successive imaging scan of the plurality of imaging scans based, at least in part, on a location of a corresponding cerebral region in a preceding imaging scan of the plurality of imaging scans, using the generated template.
In accordance with another embodiment of the instant invention, an automated method of identifying suspected lesions in a brain is provided. The method includes the following sequential, non-sequential, sequence-independent steps. A processor (a) provides a magnetic resonance image (MRI) of a patient""s head, including a plurality of slices of the patient""s head, which MRI comprises a multispectral data set that can be displayed as an image of varying pixel intensities. The processor (b) identifies a brain area within each slice to provide a plurality of masked images of intracranial tissue. The processor (c) applies a segmentation technique to at least one of the masked images to classify the varying pixel intensities into separate groupings, which potentially correspond to different tissue types. The processor (d) refines the initial segmentation into the separate groupings of at least the first masked image obtained from step (c) using one or more knowledge rules that combine pixel intensities with spatial relationships of anatomical structures to locate one or more anatomical regions of the brain. The processor (e) identifies, if present, the one or more anatomical regions of the brain located in step (d) in other masked images obtained from step (c). The processor (f) further refines the resulting knowledge rule-refined images from steps (d) and (e) to locate suspected lesions in the brain.
Optionally, the magnetic resonance image includes a multispectral data set including proton density weighted (PDw), T1 weighted (T1w) and T2 weighted (T2w) acqusitions. Optionally, the slices are taken in the axial, coronal, or sagittal planes of the patient""s head. Optionally, the varying pixel intensities are classified into at least four separate groupings, which potentially correspond to at least four different tissue types, including a first tissue type, a second tissue type, a third tissue type, and a fourth tissue type. Optionally, the first tissue type comprises cerebrospinal fluid. Optionally, the second tissue type comprises white matter. Optionally, the third tissue type comprises gray matter. Optionally, the fourth tissue type comprises white matter hyperintensities.
Optionally, the anatomical regions of the brain include at least one of lateral ventricles, caudate nuclei, lenticular nuclei, hippocampus, brain stem, cerebellum, and thalamus. Optionally, the suspected lesions include hyperintense lesions embedded within the white matter. Optionally, the suspected lesions comprise a pertiventricular ring.
In accordance with another embodiment of the instant invention, an apparatus for interpreting at least one imaging scan of a patient is provided. The apparatus includes (a) first means for identifying a location of at least one cerebral region in the imaging scan based, at least in part, on a relative location of a lateral ventricle. The first identifying means (a) is free of human intervention.
Optionally, the at least one imaging scan includes a plurality of consecutive imaging scans. Optionally, the apparatus further includes (b) second means for identifying a location of each cerebral region of the at least one cerebral region in a successive imaging scan of the plurality of imaging scans based, at least in part, on a location of a corresponding cerebral region in a preceding imaging scan of the plurality of imaging scans. The second identifying means (b) is free of human intervention.
Optionally, the apparatus includes (c) means for determining a volumetric measurement for at least one of the each cerebral region. Optionally, the apparatus includes (d) third means for identifying a suspected presence of white matter lesion tissue in the imaging scan based on a knowledge base relating voxel intensity distributions and a spatial relationship of cerebral anatomical structures. Optionally, at least one cerebral region includes at least one of a caudate nucleus, a lenticular nucleus, a thalamus, a hippocampus, a periventricular ring, white matter, a brain stem, and a cerebellum.
Optionally, the first identifying means (a) includes means for determining a location of the caudate nucleus, at least in part, from the relative location of the lateral ventricle. Optionally, the first identifying means (a) includes means for determining a location of the thalamus, at least in part, from at least one of a relative location of the caudate nucleus and the relative location of the lateral ventricle. Optionally, the first identifying means (a) includes means for determining a location of the lenticular nucleus, at least in pat, from at least one of the relative location of the caudate nucleus and a relative location of the thalamus. Optionally, the first identifying means (a) includes means for determining a location of the hippocampus, at least in part, from the relative location of the lateral ventricle. Optionally, the first identifying means (a) includes means for determining a location of the periventricular ring, at least in part from at least one of the location of the lateral ventricle, the location of the caudate nucleus, and the location of the thalamus.
Optionally, the third identifying means d) includes means for identifying white matter regions in the at least one imaging scan, eliminating therefrom voxels identifiable as at least one of white matter, a perivascular space, and cerebrospinal fluid. Optionally, the white matter regions include at least one of an internal capsule, a frontal lobe, an anterior temporal lobe, an anterior parietal lobe, a posterior parietal lobe, an occipital lobe, and a posterior temporal lobe.
Optionally, the apparatus includes means for generating a template unique to the patient, the template including each identified at least one cerebral region. Optionally, the at least one imaging scan includes a plurality of consecutive imaging scans. The apparatus optionally further include (b) fourth means for identifying a location of each cerebral region of the at least one cerebral region in a successive imaging scan of the plurality of imaging scans based, at least in part, on a location of a corresponding cerebral region in a preceding imaging scan of the plurality of imaging scans, using the generated template.
In accordance with another embodiment of the instant invention, a computer readable medium including instructions being executed by a computer is provided. The instructions instruct the computer to execute an interpretation of at least one imaging scan of a patient. The instructions include the following sequential, non-sequential, or sequence-independent steps. A processor (a) identifies a location of at least one cerebral region in the imaging scan based, at least in part, on a relative location of a lateral ventricle. The identifying instruction (a) is free of human intervention.
Optionally, the at least one imaging scan includes a plurality of consecutive imaging scans. Optionally, the processor (b) identifies a location of each cerebral region of the at least one cerebral region in a successive imaging scan of the plurality of imaging scans based, at least in part, on a location of a corresponding cerebral region in a preceding imaging scan of the plurality of imaging scans. The identifying instruction (b) is free of human intervention.
Optionally, the processor (c) determines a volumetric measurement for at least one of the each cerebral region.
Optionally, the processor (d) identifies a suspected presence of white matter lesion tissue in the imaging scan based on a knowledge base relating voxel intensity distributions and a spatial relationship of cerebral anatomical structures.
Optionally, at least one cerebral region includes at least one of a caudate nucleus, a lenticular nucleus, a thalamus, a hippocampus, a periventricular ring, white matter, a brain stem, and a cerebellum. Optionally, the identifying instruction (a) includes determining a location of the caudate nucleus, at least in part, from the relative location of the lateral ventricle. Optionally, the identifying instruction (a) includes determining a location of the thalamus, at least in part, from at least one of a relative location of the caudate nucleus and the relative location of the lateral ventricle. Optionally, the identifying instruction (a) includes determining a location of the lenticular nucleus, at least in part, from at least one of the relative location of the caudate nucleus and a relative location of the thalamus. Optionally, the identifying instruction (a) includes determining a location of the hippocampus, at least in part, from the relative location of the lateral ventricle. Optionally, the identifying instruction (a) includes determining a location of the periventricular ring, at least in part from at least one of the location of the lateral ventricle, the location of the caudate nucleus, and the location of the thalamus.
Optionally, the identifying instruction (d) includes identifying white matter regions in the at least one imaging scan, eliminating therefrom voxels identifiable as at least one of white matter, a perivascular space, and cerebrospinal fluid. Optionally, the white matter regions include at least one of an internal capsule, a frontal lobe, an anterior temporal lobe, an anterior parietal lobe, a posterior parietal lobe, an occipital lobe, and a posterior temporal lobe.
Optionally, the processor generates a template unique to the patient, the template including each identified at least one cerebral region. Optionally, the at least one imaging scan includes a plurality of consecutive imaging scans. Optionally, the processor (b) identifies a location of each cerebral region of the at least one cerebral region in a successive imaging scan of the plurality of imaging scans based, at least in part, on a location of a corresponding cerebral region in a preceding imaging scan of the plurality of imaging scans, using the generated template.
In accordance with another embodiment of the instant invention, a computer system for interpreting at least one imaging scan of a patient is provided. The computer system includes a processor. The computer system also includes a memory storing a computer program controlling operation of the processor. The program includes instructions for causing the processor to effect the following sequential, non-sequential, or sequence-independent steps. The processor (a) identifies a location of at least one cerebral region in the imaging scan based, at least in part, on a relative location of a lateral ventricle. The identifying instruction (a) is free of human intervention.
Optionally, the at least one imaging scan includes a plurality of consecutive imaging scans. Optionally, the processor (b) identifies a location of each cerebral region of the at least one cerebral region in a successive imaging scan of the plurality of imaging scans based, at least in part, on a location of a corresponding cerebral region in a preceding imaging scan of the plurality of imaging scans. The identifying instruction (b) is free of human intervention. Optionally, the processor (c) determines a volumetric measurement for at least one of the each cerebral region.
Optionally, the processor (d) identifies la suspected presence of white matter lesion tissue in the imaging scan based on a knowledge base relating voxel intensity distributions and a spatial relationship of cerebral anatomical structures.
Optionally, at least one cerebral region includes at least one of a caudate nucleus, a lenticular nucleus, a thalamus, a hippocampus, a periventricular ring, white matter, a brain stem, and a cerebellum. Optionally, the identifying instruction (a) includes determining a location of the caudate nucleus, at least in part, from the relative location of the lateral ventricle. Optionally, the identifying instruction (a) includes determining a location of the thalamus, at least in part, from at least one of a relative location of the caudate nucleus and the relative location of the lateral ventricle. Optionally, the identifying instruction (a) includes determining a location of the lenticular nucleus, at least in part, from at least one of the relative location of the caudate nucleus and a relative location of the thalamus. Optionally, the identifying instruction (a) includes determining a location of the hippocampus, at least in part, from the relative location of the lateral ventricle. Optionally, the identifying instruction (a) includes determining a location of the periventricular ring, at least in part from at least one of the location of the lateral ventricle, the location of the caudate nucleus, and the location of the thalamus.
Optionally, the identifying instruction (d) includes identifying white matter regions in the at least one imaging scan, eliminating therefrom voxels identifiable as at least one of white matter, a perivascular space, and cerebrospinal fluid. Optionally, the white matter regions include at least one of an internal capsule, a frontal lobe, an anterior temporal lobe, an anterior parietal lobe, a posterior parietal lobe, an occipital lobe, and a posterior temporal lobe.
Optionally, the processor generates a template unique to the patient, the template including each identified at least one cerebral region. Optionally, the at least one imaging scan includes a plurality of consecutive imaging scans. Optionally, the processor (b) identifies a location of each cerebral region of the at least one cerebral region in a successive imaging scan of the plurality of imaging scans based, at least in part, on a location of a corresponding cerebral region in a preceding imaging scan of the plurality of imaging scans, using the generated template.
Optionally, the computer system includes a computer including the processor. The computer is optionally communicatable with a user via a computer network. Optionally, the computer includes a Web server.
In accordance with another embodiment of the instant invention, an internet appliance is provided. The internet appliance includes a thin client programmably connected via a computer network to a single web hosting facility. The single web hosting facility includes a server communicatable with a user via said thin client. The server is in communication with a processor and a computer readable medium including instructions being executed by a processor.
The instructions instruct the computer to execute an interpretation of at least one imaging scan of a patient. The instructions includes the following sequential, non-sequential, or sequence-independent steps. The processor (a) identifies a location of at least one cerebral region in the imaging scan based, at least in pat, on a relative location of a lateral ventricle. The identifying instruction (a) is free of human intervention.
Optionally, the at least one imaging scan includes a plurality of consecutive imaging scans. Optionally, the processor (b) identifies a location of each cerebral region of the at least one cerebral region in a successive imaging scan of the plurality of imaging scans based, at least in part, on a location of a corresponding cerebral region in a preceding imaging scan of the plurality of imaging scans. The identifying instruction (b) is free of human intervention. Optionally, the processor (c) determines a volumetric measurement for at least one of the each cerebral region.
Optionally, the processor (d) identifies a suspected presence of white matter lesion tissue in the imaging scan based on a knowledge base relating voxel intensity distributions and a spatial relationship of cerebral anatomical structures.
Optionally, the at least one cerebral region includes at least one of a caudate nucleus, a lenticular nucleus, a thalamus, a hippocampus, a periventricular ring, white matter, a brain stem, and a cerebellum. Optionally, the identifying instruction (a) includes determining a location of the caudate nucleus, at least in part, from the relative location of the lateral ventricle. Optionally, the identifying instruction (a) includes determining a location of the thalamus, at least in part, from at least one of a relative location of the caudate nucleus and the relative location of the lateral ventricle. Optionally, the identifying instruction (a) includes determining a location of the lenticular nucleus, at least in part, from at least one of the relative location of the caudate nucleus and a relative location of the thalamus. Optionally, the identifying instruction (a) includes determining a location of the hippocampus, at least in part, from the relative location of the lateral ventricle.
Optionally, the identifying instruction (a) includes determining a location of the periventricular ring, at least in part from at least one of the location of the lateral ventricle, the location of the caudate nucleus, and the location of the thalamus.
Optionally, the identifying instruction (d) includes identifying white matter regions in the at least one imaging scan, eliminating therefrom voxels identifiable as at least one of white matter, a perivascular space, and cerebrospinal fluid. Optionally, the white matter regions include at least one of an internal capsule, a frontal lobe, an anterior temporal lobe, an anterior parietal lobe, a posterior parietal lobe, an occipital lobe, and a posterior temporal lobe.
Optionally, the processor generates a template unique to the patient, the template including each identified at least one cerebral region. Optionally, the at least one imaging scan includes a plurality of consecutive imaging scans. Optionally, the processor (b) identifies a location of each cerebral region of the at least one cerebral region in a successive imaging scan of the plurality of imaging scans based, at least in part, on a location of a corresponding cerebral region in a preceding imaging scan of the plurality of imaging scans, using the generated template.
There has thus been outlined, rather broadly, the more important features of the invention in order that the detailed description thereof that follows may be better understood, and in order that the present contribution to the art may be better appreciated. There are, of course, additional features of the invention that will be described hereinafter and which will form the subject matter of the claims appended hereto.
In this respect, before explaining at least one embodiment of the invention in detail, it is to be understood that the invention is not limited in its application to the details of construction and to the arrangements of the components set forth in the following description or illustrated in the drawings. The invention is capable of other embodiments and of being practiced and carried out in various ways. Also, it is to be understood that the phraseology and terminology employed herein are for the purpose of description and should not be regarded as limiting.
As such, those skilled in the art will appreciate that the conception, upon which this disclosure is based, may readily be utilized as a basis for the designing of other structures, methods and systems for carrying out the several purposes of the present invention. It is important, therefore, that the claims be regarded as including such equivalent constructions insofar as they do not depart from the spirit and scope of the present invention.
Further, the purpose of the foregoing abstract is to enable the U.S. Patent and Trademark Office and the public generally, and especially the scientists, engineers and practitioners in the art who are not familiar with patent or legal terms or phraseology, to determine quickly from a cursory inspection the nature and essence of the technical disclosure of the application. The abstract is neither intended to define the invention of the application, which is measured by the claims, nor is it intended to be limiting as to the scope of the invention in any way.